Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929683858194432 |
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| author | Lu, Yuxing Goi, Sin Yee Zhao, Xukai Wang, Jinzhuo |
| author_facet | Lu, Yuxing Goi, Sin Yee Zhao, Xukai Wang, Jinzhuo |
| contents | Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. In this survey, we address this gap by offering a systematic review of BKGs from three core perspectives: domains, tasks, and applications. We begin by examining how BKGs are constructed from diverse data sources, including molecular interactions, pharmacological datasets, and clinical records. Next, we discuss the essential tasks enabled by BKGs, focusing on knowledge management, retrieval, reasoning, and interpretation. Finally, we highlight real-world applications in precision medicine, drug discovery, and scientific research, illustrating the translational impact of BKGs across multiple sectors. By synthesizing these perspectives into a unified framework, this survey not only clarifies the current state of BKG research but also establishes a foundation for future exploration, enabling both innovative methodological advances and practical implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11632 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications Lu, Yuxing Goi, Sin Yee Zhao, Xukai Wang, Jinzhuo Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Information Retrieval Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. In this survey, we address this gap by offering a systematic review of BKGs from three core perspectives: domains, tasks, and applications. We begin by examining how BKGs are constructed from diverse data sources, including molecular interactions, pharmacological datasets, and clinical records. Next, we discuss the essential tasks enabled by BKGs, focusing on knowledge management, retrieval, reasoning, and interpretation. Finally, we highlight real-world applications in precision medicine, drug discovery, and scientific research, illustrating the translational impact of BKGs across multiple sectors. By synthesizing these perspectives into a unified framework, this survey not only clarifies the current state of BKG research but also establishes a foundation for future exploration, enabling both innovative methodological advances and practical implementations. |
| title | Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications |
| topic | Computation and Language Artificial Intelligence Computational Engineering, Finance, and Science Information Retrieval |
| url | https://arxiv.org/abs/2501.11632 |